Multidimensional Clustering Algorithms
✍ Scribed by Murtagh F.
- Tongue
- English
- Leaves
- 134
- Category
- Library
No coin nor oath required. For personal study only.
✦ Synopsis
Physica Verlag, 1985. — 134 p.
The objectives of this monograph are as follows: to collect together important recent algorithmic results in the area of cluster analysis; to indicate algorithms which may be of importance in parallel computing environme~ts; to include (unlike other general texts on clustering) discussion of problems specific to the computing area such as pattern recognition and information storage and retrieval; and to clearly describe clustering algorithms which are of general, practical relevance.Algorithms and ApplicationsFast Nearest Neighbour Sfarching
Synoptic Clustering
Connectivity Clustering
New Clustering Problems
✦ Subjects
Информатика и вычислительная техника;Искусственный интеллект;Интеллектуальный анализ данных
📜 SIMILAR VOLUMES
<p>This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The goal of this volume is to summarize the state-of-the-art in partitional clustering. The book includes such topics as center-based clustering, competitive learni
Springer, 2015. — 420 p. — ISBN-10: 3319092588, ISBN-13: 978-3-319-09258-4.<br/>На англ. языке.<div class="bb-sep"></div>Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining i
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anom
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anom